Abstract THE CARE FACTOR: THE IMPORTANCE OF USING DOMAIN IDENTIFICATION MEASURES TO IDENTIFY STEREOTYPE THREAT VULNERABILITY IN HIGH SCHOOL FEMALES. By Kimberly Y. Randolph, PhD A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2016. Major Director: Dr. Charol Shakeshaft, PhD. Professor Department of Educational Leadership There is a gap that persists in math achievement between high school males and females. Although the NAEP 2015 longitudinal report shows a narrowing in math achievement between males and females there still remains a gap. More importantly, females are less likely to enter into math - related college programs. Those that do enter into math related college programs sometimes choose to drop out of these programs. The purpose of this study was to do an analysis of factors, along with stereotype threat (the fear of doing something that would inadvertently confirm that stereotype) that might affect math achievement in girls. The factors used were domain identification measure, motivational orientation, self-efficacy and cognitive interference. Girls who participated in the study attended private or public schools, are in the 9th through 12 grade and have taken algebra I and geometry. Thus, for the female students in this study, domain identification level did not predict math achievement, desire to learn did not predict math achievement, mastery of goals did not predict math achievement, competitive excellence did not predict math achievement, competition seeking predicted math achievement accounting for 25% of the variance in math scores, motivation anxiety worry did not predict math achievement, and motivation anxiety emotionality did not predict math achievement, threat condition did not affect self- efficacy, threat condition did not affect cognitive interference, threat is not related to desire to learn, threat has no effect on personal mastery, threat condition had no effect on competition seeking, threat condition had no effect on emotionality, threat condition did not have an effect on mathematics achievement. . Perhaps there have been changes in female student attitude toward mathematics. With the rise of (Science Technology Engineering Mathematics) initiatives in schools across the country, females are encouraged to immerse themselves in domains that traditionally are dominated by males.

Approaching parameter estimation from the discrete-time domain is the dominating paradigm in system identification. Identification of continuous-time models on the other hand is motivated by the fact that modelling of physical systems often take place in continuous-time. For many practical applications there is also a genuine interest in the parameters connected to these physical models. The most important element of time- and frequency-domain identification from sampled data is the discrete-time system, which is connected to the parameters of the underlying continuous-time system. For input-output models, it governs the frequency response from the sampled input to the sampled output. In case of time series, it models the spectrum of the sampled output. As the rate of sampling increase, the relationship between the discrete- and continuous-time parameters can become more or less ill-conditioned. Mainly, because the gathering of the poles of the discrete-time system around the value 1 in the complex plane will produce numerical difficulties while mapping back to the continuous-time parameters. We will therefore investigate robust alternatives to using the exact discrete-time system, which are based on more direct use of the continuous-time system. Another, maybe more important, reason for studying such approximations is that they will provide insight into how one can deal with non-uniformly sampled data. An equally important issue in system identification is the effect of model choice. The user might not know a lot about the system to begin with. Often, the model will only capture a particular aspect of the data which the user is interested in. Deviations can, for instance, be due to mis-readings while taking measurements or un-modelled dynamics in the case of dynamical systems. They can also be caused by misunderstandings about the continuous-time signal that underlies sampled data. From a user perspective, it is important to be able to control how and to what extent these un-modelled aspects influence the quality of the intended model. The classical way of reducing the effect of modelling errors in statistics, signal processing and identification in the time-domain is to introduce a robust norm into the criterion function of the method. The thesis contains results which quantify the effect of broad-band disturbances on the quality of frequency-domain parameter estimates. It also contains methods to reduce the effect of narrow-band disturbances or frequency domain outliers on frequency-domain parameter estimates by means of methods from robust statistics.

The first purpose of this research was to understand the factors that contribute to the underrepresentation of women of color in science, technology, engineering, and math (STEM) career fields, with a specific focus on understanding the issues faced by Black women. The second purpose was to analyze the gender and racial differences in the interrelationship of high school students' class perceptions, math identification, and their decisions to select a STEM major in college. These relationships are important because the growth outlook for careers in STEM is higher than other career fields and providing underrepresented women in STEM equal access to these careers is vital. Additionally, it is important to increase the gender and racial diversity of the STEM workforce. The first manuscript (Chapter 2) was based on a literature review that provided an overview of the social factors that affect the underrepresentation of women of color in STEM by highlighting the experiences of girls of color in their youth. The literature review explored the stereotypes and discrimination girls of color experienced in the K-12 school system that affect their participation in math and science, and how the intersection of race and gender contributed to the experiences of Black females when it comes to their experiences with STEM. Using the MUSIC model of motivation theory and domain identification theory, the study in Chapter 3 explored the relationship between students' math class perceptions, math identification, and decision to major in a math-intensive (i.e., math and statistics, engineering, computer science, and physical sciences) STEM major. The study in Chapter 3 used multigroup structural equation modeling (SEM) to analyze gender and racial differences among four groups Black females, Black males, White females, and White males. The results indicate that students' math class perceptions of success and interest were positively related to their math identification. Furthermore, students' math identification was positively related to their decisions to select a STEM major as they entered college. The interrelationship between math class perceptions, math identification, and the decision to select a STEM major was not statistically different for Black females as compared to Black males, White females, or White males. Implications for schools and educators for increasing the participation of Black female students in STEM are discussed. / Doctor of Philosophy / The purposes of this research were to understand the factors that contribute to the underrepresentation of women of color in science, technology, engineering, and math (STEM) and to analyze the gender and racial differences in the interrelationship of students' class perceptions, math identification, and decisions to select a STEM major. Because Black women are one of the least represented groups in careers associated with STEM, one of the aims of this research was to investigate some of the factors contributing to Black women's underrepresentation in STEM careers. The research was conducted in two parts. The first manuscript (Chapter 2) was based on a literature review that provided an overview of the social factors that affect the underrepresentation of women of color in STEM careers by highlighting their experiences in their youth. The literature review explored discrimination against girls of color in the K-12 school system, especially as it relates to math and science participation, and how the intersection of race and gender contributed to the experiences of Black females in STEM. The second manuscript (Chapter 3) used the MUSIC model of motivation theory and domain identification theory to analyze the relationship between students' math class perceptions, how well students see math as part of their identity (math identification), and decision to major in a math-intensive (math and statistics, engineering, computer science, and physical sciences) STEM major. I used a statistical method, structural equation modeling (SEM), to analyze gender and racial differences among four groups; Black females, Black males, White females, and White males. The math class perceptions of success and interest were found to be positively related to math identification. Students' math identification was found to be positively related to their decisions to select a STEM major. Black females did not show a difference from the other groups in the relationship between their math class perceptions, math identification, and decision to select a STEM major. Implications for schools and educators for increasing the participation of Black female students in STEM are discussed.

This dissertation examined the differences in group mean scores of traditional and pilot groups on the students' motivational beliefs and their intention to pursue majors and careers in engineering. The difference between the two groups was in terms of instruction techniques used. The instructional techniques used for the traditional group was that of traditional engineering design (TED), while the technique used for the pilot group had more features of an active learning approach. Further, it tested the tenability of the domain identification model. The domain identification model was used to understand students' decision-making processes in committing to engineering majors and engineering careers. The data for this study was collected via online survey from first-year engineering students enrolled in an introductory engineering course at a research-intensive university located in southeastern US. The sample sizes of the traditional group and pilot group at the beginning of the semester were 875 and 188, respectively. The sample sizes of the traditional group and pilot group at the end of the semester were 812 and 242, respectively. The mean differences between the two groups were computed using t-tests via SPSS version 22.0. The causality hypothesized among variables in the domain identification model were tested using structural equation modeling (SEM) techniques. The measurement and structural models were estimated using LISREL version 9.1. This study followed the two-step SEM approach that Anderson and Gerbing (1988) suggested. A measurement model with an acceptable fit to the data was obtained followed by an estimation and evaluation of structural models. All the independent sample t-tests were not statistically significant indicating that the mean scores of students in the two groups did not differ significantly on any of the motivational and intention variables. The hypothesized measurement and structural models provided a good fit to the data. A few post-hoc revisions were made to the models. This study brought empirical evidence that the domain identification model can be used to understand students' major-and career-decision making processes. Engineering identification was a better predictor of major intention and career intention compared to engineering program utility, engineering program belonging, and engineering program expectancy. / Ph. D.

This dissertation examined the extent to which pre-high school students' motivation-related perceptions of their science class affected their science identification, which sequentially affected their future science-related career goals. The MUSIC® Model of Motivation (Jones, 2009, 2018) includes five components (i.e., eMpowerment, Usefulness, Success, Interest, and Caring) and is designed to help teachers design instruction to promote students' motivation. Domain identification (Osborne and Jones, 2011) is a concept closely related to students' motivation and academic outcomes. In this study, data was collected from 311 pre-high school students and Structural Equation Modeling (SEM) analysis was conducted to test the structure pattern among the MUSIC model components, science identification, and science-related career goals. Results indicate that with three of the MUSIC model components (i.e., usefulness, success, and interest) significantly related to students' science identification, students' science identification was highly correlated to their science career goals. Moreover, this study demonstrated the structure patterns among the MUSIC model components and science identification varied by gender by conducting multi-group SEM analyses for a separate female sample (N = 161) and male sample (N = 150). Consistently, students' science identification was a strong predictor of their science career goals in both female and male groups. These findings are important for STEM educators because they indicate that it may be possible for teachers to impact students' science identification and career goals by focusing on students' perceptions of the MUSIC model components in science class. Moreover, these results contribute to the study of the large gender gap in STEM careers. Teachers can focus on specific teaching strategies and help female students develop their science identification in ways that lead to their long-term science-related career goals. / Ph. D. / This dissertation examined the extent to which pre-high school students’ motivation-related perceptions of their science class affected their science identification, which sequentially affected their future science-related career goals. Science identification is a concept that describes the extent to which a student values science as an important part of his or her self (Osborne & Jones, 2011). One goal of this study examined how students’ perceptions of their science class affected students’ science identification. Specifically, this study focused on students’ perceptions of the five components of the MUSIC® Model of Motivation (Jones, 2009, 2018): eMpowerment, Usefulness, Success, Interest, and Caring. The MUSIC model was developed to help teachers design instruction to promote students’ motivation. In this study, results indicate that with three of the MUSIC model components (i.e., usefulness, success, and interest) significantly related to students’ science identification, students’ science identification was highly correlated to their science career goals. Moreover, this study reveals that the structure patterns among the MUSIC model components and science identification varied by gender. Consistently, students’ science identification was a strong predictor of their science career goals in both female and male groups. These findings are important for STEM educators because they indicate that it may be possible for teachers to impact students’ science identification and career goals by focusing on students’ perceptions of the MUSIC model components in science class. Moreover, these results contribute to the study of the large gender gap in STEM careers, in which females are underrepresented. Teachers can focus on specific teaching strategies and help female students develop their science identification in ways that lead to their long-term science-related career goals.

Industrirobotar är idag en väsentlig del i tillverkningsindustrin där de bland annat används för att minska kostnader, öka produktivitet och kvalitet och ersätta människor i farliga eller slitsamma uppgifter. Höga krav på noggrannhet och snabbhet hos robotens rörelser innebär också höga krav på de matematiska modeller som ligger till grund för robotens styrsystem. Modellerna används där för att beskriva det komplicerade sambandet mellan robotarmens rörelser och de motorer som orsakar rörelsen. Tillförlitliga modeller är också nödvändiga för exempelvis mekanisk design, simulering av prestanda, diagnos och övervakning. En trend idag är att bygga lättviktsrobotar, vilket innebär att robotens vikt minskas men att den fortfarande kan hantera en lika tung last. Orsaken till detta är främst att minska kostnaden, men också säkerhetsaspekter spelar in. En lättare robotarm ger dock en vekare struktur där elastiska effekter inte längre kan försummas i modellen om man kräver hög prestanda. De elastiska effekterna beskrivs i den matematiska modellen med hjälp av fjädrar och dämpare. Denna avhandling handlar om hur dessa matematiska modeller kan tas fram genom systemidentifiering, vilket är ett viktigt verktyg där mätningar från robotens rörelser används för att bestämma okända parametrar i modellen. Det som mäts är position och moment hos robotens alla motorer. Identifiering av industrirobotar är ett utmanande problem bland annat eftersom robotens beteende varierar beroende på armens position. Den metod som föreslås i avhandlingen innebär att man först identifierar lokala modeller i ett antal positioner. Var och en av dessa beskriver robotens beteende kring en viss arbetspunkt. Sedan anpassas parametrarna i en global modell, som är giltig för alla positioner, så att den så väl som möjligt beskriver det lokala beteendet i de olika positionerna. I avhandlingen analyseras olika metoder för att ta fram lokala modeller. För att få bra resultat krävs att experimenten är omsorgsfullt utformade. För att minska osäkerheten i den globala modellens identifierade parametrar ingår också valet av optimala positioner för experimenten. Olika metoder för att identifiera parametrarna jämförs i avhandlingen och experimentella resultat visar användbarheten av den föreslagna metoden. Den identifierade robotmodellen ger en bra global beskrivning av robotens beteende. Resultatet av forskningen har även gjorts tillgängligt i ett datorverktyg för att noggrant kunna ta fram lokala modeller och identifiera parametrar i dynamiska robotmodeller. / Industrial robots are today essential components in the manufacturing industry where they are used to save costs, increase productivity and quality, and eliminate dangerous and laborious work. High demands on accuracy and speed of the robot motion require that the mathematical models, used in the motion control system, are accurate. The models are used to describe the complicated nonlinear relation between the robot motion and the motors that cause the motion. Accurate dynamic robot models are needed in many areas, such as mechanical design, performance simulation, control, diagnosis, and supervision. A trend in industrial robots is toward lightweight robot structures, where the weight is reduced but with a preserved payload capacity. This is motivated by cost reduction as well as safety issues, but results in a weaker (more compliant) mechanical structure with enhanced elastic effects. For high performance, it is therefore necessary to have models describing these elastic effects. This thesis deals with identification of dynamic robot models, which means that measurements from the robot motion are used to estimate unknown parameters in the models. The measured signals are angular position and torque of the motors. Identifying robot models is a challenging task since an industrial robot is a multivariable, nonlinear, unstable, and resonant system. In this thesis, the unknown parameters (typically spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified, mainly in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. Each nonparametric FRF then describe the local behavior around an operating point. The nonlinear parametric robot model is linearized in the same operating points and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model). Methods for estimating the nonparametric FRF from experimental data are analyzed with respect to bias, variance, and nonlinearities. In order to accurately estimate the nonparametric FRF, the experiments must be carefully designed. To minimize the uncertainty in the estimated parameters, the selection of optimal robot configurations/positions for the experiments is also part of the design. Different parameter estimators are compared in the thesis and experimental results show the usefulness of the proposed identification procedure. The identified nonlinear robot model gives a good global description of the dynamics in the frequency range of interest. The research work is also implemented and made easily available in a software tool for accurate estimation of nonparametric FRFs as well as parametric robot models.

Viscoelastic materials can today be found in a wide range of practical applications. In order to make efficient use of these materials in construction, it is of importance to know how they behave when subjected to dynamic load. Characterization of viscoelastic materials is therefore an important topic, that has received a lot of attention over the years. This thesis treats different methods for identifying the complex modulus of an viscoelastic material. The complex modulus is a frequency dependent material function, that describes the deformation of the material when subjected to stress. With knowledge of this and other material functions, it is possible to simulate and predict how the material behaves under different kinds of dynamic load. The complex modulus is often identified through wave propagation testing, where the viscoelastic material is subjected to some kind of load and the response then measured. Models describing the wave propagation in the setups are then needed. In order for the identification to be accurate, it is important that these models can describe the wave propagation in an adequate way. A statistical test quantity is therefore derived and used to evaluate the wave propagation models in this thesis. Both nonparametric and parametric identification of the complex modulus is considered in this thesis.  An important aspect of the identification is the accuracy of the estimates.  Theoretical expressions for the variance of the estimates are therefore derived, both for the nonparametric and the parametric identification. In order for the identification to be as accurate as possible, it is also important that the experimental data contains as much valuable information as possible. Different experimental conditions, such as sensor locations and choice of excitation, can influence the amount of information in the data. The procedure of determining optimal values for such design parameters is known as optimal experiment design. In this thesis, both optimal sensor locations and optimal excitation are considered.

La problématique traitée dans cette thèse concerne la commande de robots manipulateurs à articulations flexibles. Les méthodes développées visent à satisfaire les spécifications de performance et de robustesse en suivi de trajectoire, ainsi qu’à assurer un niveau de sécurité compatible avec un scénario de fonctionnement interactif dans lequel l’homme et le robot partagent un même espace de travail. Seules les mesures moteur sont utilisées dans un contexte d’instrumentation réduite. Le premier objectif de performance de la commande de mouvement est atteint grâce à l’identification expérimentale d’un modèle flexible représentatif du système, et l’usage de ce modèle pour la synthèse de lois de commande avancées intégrées au sein d’une structure cascade. Deux approches complémentaires fondées d’une part sur la commande prédictive de type GPC (Generalized Predictive Control), et d’autre part sur la commande Hinfini, sont considérées pour la synthèse de lois de commande à deux degrés de liberté, prédictives et robustes. Les performances de ces deux approches sont analysées et évaluées expérimentalement. Le deuxième objectif de sécurité est abordé à travers un algorithme de détection de collisions du robot avec son environnement, sans capteur d’effort et en présence d’incertitudes de modélisation. Afin de séparer efficacement les effets dynamiques des collisions de ceux des erreurs de modélisation, une stratégie adaptative de filtrage et de décision tenant compte de l’état du système est proposée. La validation expérimentale montre une très bonne sensibilité de détection, compatible avec les normes et les recommandations de sécurité relatives à la robotique collaborative. / The present thesis addresses the problem of motion control of flexible-joint robot manipulators using motor sensors only. The global objective is to guarantee tracking performance and robustness with respect to modeling uncertainties, together with safe human-robot interaction in a collaborative scenario where the robot and the human operator share the same workspace. The first objective of performance is achieved through the experimental identification of a flexible model of the system and the use of this model for the design of advanced control laws implemented in a cascade structure. Two complementary approaches, based either on predictive (Generalized Predictive Control, GPC) or Hinfinity control frameworks, are considered to design predictive and robust two degrees-of-freedom controllers. Experimental evaluation and analysis of the proposed strategies is provided. The second objective of safety is addressed by a novel algorithm for human-robot collision detection, without force sensors and in the presence of modeling uncertainties. In order to efficiently separate the dynamic effects of the collisions from the effects due to modeling errors, the proposed approach includes adaptive filtering and uses dynamic thresholds depending on the robot state. Experimental evaluation demonstrates a good detection sensitivity which is consistent with safety standards and recommendations for collaborative robotics.